Local Track Irregularity Identification Based on Multi-Sensor Time–Frequency Features of High-Speed Railway Bridge Accelerations

Shortwave track diseases are generally reflected in the form of local irregularity. Such will greatly impact train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize irregularities based on multi-sensor time–frequency features high-speed railway bridge accelerations. Continuous wavelet transform (CWT) was used analyze accelerations bridges. Moreover, sum coefficients were proposed, considering influence distance from measurement points irregularity recognition accuracy. Then, multi-domain utilized recognize deteriorated locations. A simply-supported traversed by adopted as numerical simulation. Comparative studies conducted investigate vehicle speeds location algorithm. Numerical simulation results show that algorithm can locate accurately robust speeds.